
"""simple docstring"""






def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		str			)					->						Optional[int]:

	'''simple docstring'''




	lowerCAmelCase												=  0
	lowerCAmelCase												=  len(_lowerCamelCase			) - 1

	while left <= right:
		# avoid divided by 0 during interpolation
		if sorted_collection[left] == sorted_collection[right]:
			if sorted_collection[left] == item:
				return left
			else:
				return None

		lowerCAmelCase												=  left + ((item - sorted_collection[left]) * (right - left)) // (
		    sorted_collection[right] - sorted_collection[left]
		)

		# out of range check
		if point < 0 or point >= len(_lowerCamelCase			):
			return None

		lowerCAmelCase												=  sorted_collection[point]
		if current_item == item:
			return point
		else:
			if point < left:
				lowerCAmelCase												=  left
				lowerCAmelCase												=  point
			elif point > right:
				lowerCAmelCase												=  right
				lowerCAmelCase												=  point
			else:
				if item < current_item:
					lowerCAmelCase												=  point - 1
				else:
					lowerCAmelCase												=  point + 1
	return None




def __magic_name__	(				_lowerCamelCase:		int,					_lowerCamelCase:		Any,					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		Union[str, Any]			)					->						Any:

	'''simple docstring'''




	if sorted_collection[left] == sorted_collection[right]:
		if sorted_collection[left] == item:
			return left
		else:
			return None

	lowerCAmelCase												=  left + ((item - sorted_collection[left]) * (right - left)) // (
	    sorted_collection[right] - sorted_collection[left]
	)

	# out of range check
	if point < 0 or point >= len(_lowerCamelCase			):
		return None

	if sorted_collection[point] == item:
		return point
	elif point < left:
		return interpolation_search_by_recursion(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
	elif point > right:
		return interpolation_search_by_recursion(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
	else:
		if sorted_collection[point] > item:
			return interpolation_search_by_recursion(
			    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					point - 1			)
		else:
			return interpolation_search_by_recursion(
			    _lowerCamelCase,					_lowerCamelCase,					point + 1,					_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		Dict			)					->						int:

	'''simple docstring'''




	if collection != sorted(_lowerCamelCase			):
		raise ValueError('''Collection must be ascending sorted'''			)
	return True


if __name__ == "__main__":
					import sys
					UpperCAmelCase        =				0
					if debug == 1:
										UpperCAmelCase        =				[1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
										try:
															__assert_sorted(collection)
										except ValueError:
															sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
										UpperCAmelCase        =				6_7

					UpperCAmelCase        =				interpolation_search(collection, target)
					if result is not None:
										print(f"""{target} found at positions: {result}""")
					else:
										print("""Not found""")




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


